3 citations · 6 across the 5 of their papers we have counts for
5 papers
Analysing Cross-Lingual Transfer in Low-Resourced African Named Entity Recognition
Michael Beukman, Manuel Fokam
Transfer learning has led to large gains in performance for nearly all NLP tasks while making downstream models easier and faster to train. This has also been extended to low-resou…
MasakhaNER 2.0: Africa-centric Transfer Learning for Named Entity Recognition
David Ifeoluwa Adelani, Graham Neubig, Sebastian Ruder +42
African languages are spoken by over a billion people, but are underrepresented in NLP research and development. The challenges impeding progress include the limited availability o…
Separating Grains from the Chaff: Using Data Filtering to Improve Multilingual Translation for Low-Resourced African Languages
Idris Abdulmumin, Michael Beukman, Jesujoba O. Alabi +8
We participated in the WMT 2022 Large-Scale Machine Translation Evaluation for the African Languages Shared Task. This work describes our approach, which is based on filtering the…
The Impact of Data Corruption on Named Entity Recognition for Low-resourced Languages
Manuel Fokam, Michael Beukman
Data availability and quality are major challenges in natural language processing for low-resourced languages. In particular, there is significantly less data available than for hi…
A Few Thousand Translations Go a Long Way! Leveraging Pre-trained Models for African News Translation
David Ifeoluwa Adelani, Jesujoba Oluwadara Alabi, Angela Fan +42
Recent advances in the pre-training of language models leverage large-scale datasets to create multilingual models. However, low-resource languages are mostly left out in these dat…